SoroushVahidi/scidocs huggingface dataset:
Food image analysis: Segmentation, identification and weight estimation
(2013)

We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, we describe the image analysis tools to determine the regions where a particular food is located (image segmentation), identify the food type (feature classification) and estimate the weight of the food item (weight estimation). An image segmentation and classification system is proposed to improve the food segmentation and identification accuracy. We then estimate the weight of food to extract the nutrient content from a single image using a shape template for foods with regular shapes and area-based weight estimation for foods with irregular shapes.
Developing an Assessment Method of Active Aging: University of Jyvaskyla Active Aging Scale.
(2018)

OBJECTIVE
To develop an assessment method of active aging for research on older people.


METHOD
A multiphase process that included drafting by an expert panel, a pilot study for item analysis and scale validity, a feedback study with focus groups and questionnaire respondents, and a test-retest study. Altogether 235 people aged 60 to 94 years provided responses and/or feedback.


RESULTS
We developed a 17-item University of Jyvaskyla Active Aging Scale with four aspects in each item (goals, ability, opportunity, and activity; range 0-272). The psychometric and item properties are good and the scale assesses a unidimensional latent construct of active aging.


DISCUSSION
Our scale assesses older people's striving for well-being through activities pertaining to their goals, abilities, and opportunities. The University of Jyvaskyla Active Aging Scale provides a quantifiable measure of active aging that may be used in postal questionnaires or interviews in research and practice.
A comparison of radial and axial flux structures in electrical machines
(2009)

This paper explores the comparative advantages and disadvantages of the simplest form of axial flux geometry, the single sided version, compared to the most common form of radial flux geometry with an inside rotor, when considering permanent magnet synchronous machines. A detailed literature review of comparisons is presented. New material is presented highlighting the benefits of single sided axial flux geometry. The constraints and assumptions made when comparing possibilities are discussed in detail, including a study of the biases these can introduce. The basis of this comparison is founded on constant electromagnetic airgap shear stress, being the product of electric loading and magnetic loading, and indeed the constancy of both of those factors. The metric used for comparison is simply that of the masses of the active materials, steel, copper, and magnet material. Examples are presented, including a machine which recently went into production in quantity. A range of lesser issues which are relevant when one is making a choice, are presented and discussed.
Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits
(2017)

A conventional collaborative ltering approach using a standard utility matrix fails to capture the aspect of matching clothing items when recommending daily fashion outts. Moreover, it is challenged by the new user cold-start problem. In this paper, we describe a novel approach for guiding users in selecting daily fashion outts, by providing outt recommendations from a system consisting of an Internet of ings wardrobe enabled with RFID technology and a corresponding mobile application. We show where a conventional collaborative ltering approach comes short when recommending fashion outts, and how our novel approach—powered by machine learning algorithms—shows promising results in the domain of fashion recommendation. Evaluation of our novel approach using a real-world dataset demonstrates the system’s eectiveness and its ability to provide daily outt recommendations that are relevant to the users. A non-comparable evaluation of the conventional approach is also given.
The Effect of Using E-Learning Tools in Online and Campus-based Classrooms on Student Performance
(2011)

Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual’s perception of how the adoption of a new technology will increase their efficiency, and the individual’s perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance.
Simple and Space-Efficient Minimal Perfect Hash Functions
(2007)

A perfect hash function (PHF) h : U → [0, m − 1] for a key set S is a function that maps the keys of S to unique values. The minimum amount of space to represent a PHF for a given set S is known to be approximately 1.44n/m bits, where n = |S|. In this paper we present new algorithms for construction and evaluation of PHFs of a given set (for m = n and m = 1.23n), with the following properties: 1. Evaluation of a PHF requires constant time. 2. The algorithms are simple to describe and implement, and run in linear time. 3. The amount of space needed to represent the PHFs is around a factor 2 from the information theoretical minimum. No previously known algorithm has these properties. To our knowledge, any algorithm in the literature with the third property either: – Requires exponential time for construction and evaluation, or – Uses near-optimal space only asymptotically, for extremely large n. Thus, our main contribution is a scheme that gives low space usage for realistic values of n. The main technical ingredient is a new way of basing PHFs on random hypergraphs. Previously, this approach has been used to design simple PHFs with superlinear space usage. ⋆ This work was supported in part by GERINDO Project–grant MCT/CNPq/CTINFO 552.087/02-5, and CNPq Grants 30.5237/02-0 (Nivio Ziviani) and 142786/2006-3 (Fabiano C. Botelho) 3 This version of the paper is identical to the one published in the WADS 2007 proceedings. Unfortunately, it does not give reference and credit to the paper The Bloomier Filter: An Efficient Data Structure for Static Support Lookup Tables, by Chazelle et al., Proceedings of SODA 2004. They present a way of constructing PHFs that is equivalent to ours. It is explained as a modification of the “Bloomier Filter” data structure at the end of Section 3.3, but they do not make explicit that a PHF is constructed. Thus, the simple construction of a PHF described must be attributed to Chazelle et al. The new contribution of this paper is to analyze and optimize the constant of the space usage considering implementation aspects as well as a way of constructing MPHFs from that PHFs.
Fast and Concurrent RDF Queries with RDMA-Based Distributed Graph Exploration
(2016)

Many public knowledge bases are represented and stored as RDF graphs, where users can issue structured queries on such graphs using SPARQL. With massive queries over large and constantly growing RDF data, it is imperative that an RDF graph store should provide low latency and high throughput for concurrent query processing. However, prior systems still experience high perquery latency over large datasets and most prior designs have poor resource utilization such that each query is processed in sequence. We present Wukong, a distributed graph-based RDF store that leverages RDMA-based graph exploration to provide highly concurrent and low-latency queries over large data sets. Wukong is novel in three ways. First, Wukong provides an RDMA-friendly distributed key/value store that provides differentiated encoding and fine-grained partitioning of graph data to reduce RDMA transfers. Second, Wukong leverages full-history pruning to avoid the cost of expensive final join operations, based on the observation that the cost of one-sided RDMA operations is largely oblivious to the payload size to a certain extent. Third, countering conventional wisdom of preferring migration of execution over data, Wukong seamlessly combines data migration for low latency and execution distribution for high throughput by leveraging the low latency and high throughput of onesided RDMA operations, and proposes a worker-obliger model for efficient load balancing. Evaluation on a 6-node RDMA-capable cluster shows that Wukong significantly outperforms state-of-the-art systems like TriAD and Trinity.RDF for both latency and throughput, usually at the scale of orders of magnitude. Short for Sun Wukong, who is known as the Monkey King and is a main character in the Chinese classical novel “Journey to the West”. Since Wukong is known for his extremely fast speed (21,675 kilometers in one somersault) and the ability to fork himself to do massive multi-tasking, we term our system as Wukong. The source code and a brief instruction on how to install Wukong is available at http://ipads.se.sjtu.edu.cn/projects/wukong.
Manual Evaluation of the Pelvic Floor : State of the Art and Evaluative Hypothesis
(2018)

The pelvic floor is an anatomical area where the balance of different visceral, muscular and liquid pressures play a fundamental role in the physiological pursuit of the functions of all the structures contained therein. When the balance is broken, multiple disorders and pathologies arise, requiring conservative or surgical multidisciplinary treatments. The focus of this article is to propose a manual evaluation of the musculoskeletal structures of the pelvic floor since a complete palpatory examination taking into account the muscular, articular and ligamentous aspect of the pelvic area is currently missing. The detection of the abnormal area is a determining factor to organize properly the therapeutic work because, potentially resulting in better results. According to the Authors' knowledge, this is the first article in the current scientific landscape proposing a complete manual evaluation.
A High-Power $Ka$-Band (31–36 GHz) Solid-State Amplifier Based on Low-Loss Corporate Waveguide Combining
(2008)

A method of using low-loss waveguide septum combiners is developed into a high-power -band (31-36 GHz) amplifier producing 50 W at 33 GHz (Ka-band) using 32 low-power (>2 W) solid-state amplifier modules. By using low-loss waveguide combining and a packaged monolithic microwave integrated circuit with a low-loss microstrip-to-waveguide launcher, the output loss is minimized, allowing for the overall power-combining efficiency to remain high, 80% (average insertion loss of combiner < 0.7 dB and average insertion loss of launcher <0.3 dB) over 31-36 GHz. In the past, lower power-combining efficiencies have limited the number of modules that can be combined at -band, and hence, have limited the power output. The approach demonstrated in this paper, with high power-combining efficiency, allows a very large number (32) of solid-state amplifier modules to be combined to produce high powers. Greater than 50 W was demonstrated with low power modules, but even higher powers 120 W are possible. The current approach is based on corporate combining, using low-loss waveguide septum combiners that provide isolation, maintaining the true graceful degradation of a modular solid-state amplifier system.
Ka band spatial power-combining amplifier structures
(2008)

2.24 w power-amplifier (PA) module at 35 GHz presented using broad-band spatial power-combining system. The combiner can accommodate more monolithic microwave integrated-circuit (MMIC) PA with stagger placement structure on limited microstrip space in Ka-band waveguide structure with good return losses, and heat can dissipated into aluminum carrier quickly. This combiner is based on a slotline-to-microstrip transition structure, which also serves as a four-way power combiner. The proposed 2*2 combining structure combined by vertical stacking inside the waveguide was analyzed and optimized by finite-element-method (FEM) simulations and experiments.
Novel high efficiency broadband Ku band power combiner
(2010)

High power solid-state power amplifiers require a high efficiency power dividing/combining structure to keep the power loss as low as possible. The heat sinking capability of the divider/combiner also limits its maximum output power with continues wave (CW) configuration. In this paper, we introduce a novel 8-way Ku band power divider/combiner system, it demonstrate advantages of low loss, broadband and good heat sinking capability simultaneously. As its sub-components, low loss probes for waveguide-to-microstrip transition and low loss broadband 1-to-2 power combiners are designed and fabricated. The measured back-to-back insertion loss of the whole 8-way power combiner is lower than 0.5dB in the whole Ku band, and the corresponding combining efficiency is as high as 94.5%. The simulated thermal resistance of the system is as low as 0.21°C/W, indicating the proposed power combiner is able to produce 50W of CW output power with commercial available Monolithic Microwave Integrated Circuits (MMICs).
Image-guided full waveform inversion
(2011)

Multiple problems, including high computational cost, spurious local minima, and solutions with no geologic sense, have prevented widespread application of full waveform inversion (FWI), especially FWI of seismic reflections. These problems are fundamentally related to a large number of model parameters and to the absence of low frequencies in recorded seismograms. Instead of inverting for all the parameters in a dense model, image-guided full waveform inversion inverts for a sparse model space that contains far fewer parameters. We represent a model with a sparse set of values, and from these values, we use image-guided interpolation (IGI) and its adjoint operator to compute finelyand uniformly-sampled models that can fit recorded data in FWI. Because of this sparse representation, image-guided FWI updates more blocky models, and this blockiness in the model space mitigates the absence of low frequencies in recorded data. Moreover, IGI honors imaged structures, so image-guided FWI built in this way yields models that are geologically sensible.
Neural Network-Based Spectrum Estimation for Online WPE Dereverberation
(2017)

In this paper, we propose a novel speech dereverberation framework that utilizes deep neural network (DNN)-based spectrum estimation to construct linear inverse filters. The proposed dereverberation framework is based on the state-of-the-art inverse filter estimation algorithm called weighted prediction error (WPE) algorithm, which is known to effectively reduce reverberation and greatly boost the ASR performance in various conditions. In WPE, the accuracy of the inverse filter estimation, and thus the deverberation performance, is largely dependent on the estimation of the power spectral density (PSD) of the target signal. Therefore, the conventional WPE iteratively performs the inverse filter estimation, actual dereverberation and the PSD estimation to gradually improve the PSD estimate. However, while such iterative procedure works well when sufficiently long acoustically-stationary observed signals are available, WPE’s performance degrades when the duration of observed/accessible data is short, which typically is the case for real-time applications using online block-batch processing with small batches. To solve this problem, we incorporate the DNN-based spectrum estimator into the framework ofWPE, because a DNN can estimate the PSD robustly even from very short observed data. We experimentally show that the proposed framework outperforms the conventional WPE, and improves the ASR performance in real noisy reverberant environments in both single-channel and multichannel cases.
Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks
(2013)

In recent years, there has been a growing interest towards the application of artificial intelligence approaches in software engineering (SE) processes. In the specific area of SE for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and AI. However, just few significant results have been published. This paper briefly studies uncertainty in SASs and surveys techniques that have been developed to engineer SASs in order to tackle uncertainty. In particular, we highlight techniques that use AI concepts. We also report and discuss our own experience using Dynamic Decision Networks (DDNs) to model and support decision-making in SASs while explicitly taking into account uncertainty. We think that Bayesian inference, and specifically DDNs, provide a useful formalism to engineer systems that dynamically adapt themselves at runtime as more information about the environment and the execution context is discovered during execution. We also discuss partial results, challenges and future research avenues.
Data-driven 3D visual pronunciation of Chinese IPA for language learning
(2013)

In the framework of intelligent aided language learning, a real-time data-driven 3D visual pronunciation system of Chinese IPA is proposed. First, a high quality articulatory speech corpus including speech and 3D articulatory data of lips, tongue and jaw movements is collected through Electro-Magnetic Articulograph; second, the 3D articulatory modeling including shape design and motion synthesis is conducted. The articulatory shape is obtained by designing a precise 3D facial model including internal and external articulators. The articulatory motion synthesis is obtained combining parameterized model and anatomical model. The system can thus illustrate the slight differences among phonemes by synthesizing both internal and external articulatory movements. The perceptual evaluation shows the suitability of the system for instructing language learners to articulate.
Wearable impedance analyzer based on AD5933
(2014)

In this paper a system for measuring impedance, based on AD5933 circuit is presented. The impedance measuring range is between 9 Ω and 18 MΩ for a 1 kHz ÷ 100 kHz frequency range. Possibilities of expanding this range of measurement are also presented in the paper. The system calibration is automatic and the relative error of the impedance modulus measurement is in the range ±2%. Measured impedance main parameters are shown locally on an OLED display but can also be stored in an SD memory card for further processing. The system is portable, modular and adaptable to a large number of applications.
Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles
(2013)

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.
AUGMENTED REALITY VISUALIZATION TOOL FOR KINGS STORMWATER BRIDGE
(2004)

Augmented Reality (AR) is an emerging visualization technology that promises to change the way people see the world. The main goal of AR is to “enhance” a person’s vision of the real world with useful information about the surrounding environment. Primarily this can be done through the use of a portable computer such as a laptop and a head-mounted display (HMD) unit. When such technology is made available to scientists and engineers, Augmented Reality can provide them with the freedom to do scientific analysis at the actual location of an experiment. With this idea in mind, the goal of this paper is to investigate the development of an AR system as a visualization tool for structural engineers. This particular application will focus on monitoring the “health” of the Kings Stormwater Bridge structure. Unfortunately, since AR is still in its infancy, much of the research devoted to it has focused mainly on technical issues rather than on the development of how this technology would be used by scientists. This has led to the development of “functional” AR systems that primarily can only be operated by the developer of the system ([1], [2], [3], [12], [14]). This paper avoids this trend by focusing on the design of the visualization system so that it is useful for structural engineers. As a result, this should produce a more robust Augmented Reality system that can be used by many different users. Thus, it is the goal of this paper to show how scientists would benefit by designing a visualization system with their needs in mind.
PSO-based optimization for isolated intersections signal timings and simulation
(2008)

In this paper, based on the analyzing the signal control problem of single intersections, a kind of real-time optimization method with PSO (particle swarm optimization) solving signal timings for single intersection is put forward. On detecting the flow of current cycle and the cycle before, estimating the flow of next circle is proceeded. Setting least delay as performance index, we get each phase time for the next cycle. A simulation experiment for the traffic model at a four-phase intersection is also performed. The result shows that the method is efficient.
Android anti-forensics through a local paradigm
(2010)

Mobile devices are among the most disruptive technologies of the last years, gaining even more diffusion and success in the daily life of a wide range of people categories. Unfortunately, while the number of mobile devices implicated in crime activities is relevant and growing, the capability to perform the forensic analysis of such devices is limited both by technological and methodological problems. In this paper, we focus on Anti-Forensic techniques applied to mobile devices, presenting some fully automated instances of such techniques to Android devices. Furthermore, we tested the effectiveness of such techniques versus both the cursory examination of the device and some acquisition tools. a 2010 Digital Forensic Research Workshop. Published by Elsevier Ltd. All rights reserved.
Deriving common malware behavior through graph clustering
(2011)

Detection of malicious software (malware) continues to be a problem as hackers devise new ways to evade available methods. The proliferation of malware and malware variants requires methods that are both powerful, and fast to execute. This paper proposes a method to derive the common execution behavior of a family of malware instances. For each instance, a graph is constructed that represents kernel objects and their attributes, based on system call traces. The method combines these graphs to develop a supergraph for the family. This supergraph contains a subgraph, called the HotPath, which is observed during the execution of all the malware instances. The proposed method is scalable, identifies previously-unseen malware instances, shows high malware detection rates, and false positive rates close to 0%.